Overview

Dataset statistics

Number of variables23
Number of observations4325
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory758.0 B

Variable types

Categorical10
Numeric13

Alerts

Date has a high cardinality: 1278 distinct valuesHigh cardinality
Production Date has a high cardinality: 1405 distinct valuesHigh cardinality
Expiration Date has a high cardinality: 1441 distinct valuesHigh cardinality
Product ID is highly overall correlated with Product Name and 1 other fieldsHigh correlation
Quantity (liters/kg) is highly overall correlated with Total Value and 3 other fieldsHigh correlation
Price per Unit is highly overall correlated with Total Value and 1 other fieldsHigh correlation
Total Value is highly overall correlated with Quantity (liters/kg) and 5 other fieldsHigh correlation
Shelf Life (days) is highly overall correlated with Product Name and 1 other fieldsHigh correlation
Quantity Sold (liters/kg) is highly overall correlated with Quantity (liters/kg) and 2 other fieldsHigh correlation
Price per Unit (sold) is highly overall correlated with Price per Unit and 1 other fieldsHigh correlation
Approx. Total Revenue(INR) is highly overall correlated with Quantity (liters/kg) and 2 other fieldsHigh correlation
Quantity in Stock (liters/kg) is highly overall correlated with Quantity (liters/kg) and 1 other fieldsHigh correlation
Product Name is highly overall correlated with Product ID and 2 other fieldsHigh correlation
Storage Condition is highly overall correlated with Product ID and 2 other fieldsHigh correlation
Date is uniformly distributedUniform
Production Date is uniformly distributedUniform
Expiration Date is uniformly distributedUniform

Reproduction

Analysis started2023-09-05 12:52:52.033480
Analysis finished2023-09-05 12:53:42.923387
Duration50.89 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Location
Categorical

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size278.2 KiB
Delhi
525 
Chandigarh
519 
Uttar Pradesh
276 
Gujarat
267 
Karnataka
261 
Other values (10)
2477 

Length

Max length14
Median length11
Mean length8.830289
Min length5

Characters and Unicode

Total characters38191
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTelangana
2nd rowUttar Pradesh
3rd rowTamil Nadu
4th rowTelangana
5th rowMaharashtra

Common Values

ValueCountFrequency (%)
Delhi 525
12.1%
Chandigarh 519
12.0%
Uttar Pradesh 276
 
6.4%
Gujarat 267
 
6.2%
Karnataka 261
 
6.0%
Madhya Pradesh 259
 
6.0%
Rajasthan 256
 
5.9%
Maharashtra 255
 
5.9%
Haryana 253
 
5.8%
Kerala 249
 
5.8%
Other values (5) 1205
27.9%

Length

2023-09-05T18:23:43.122653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 535
 
10.0%
delhi 525
 
9.9%
chandigarh 519
 
9.7%
uttar 276
 
5.2%
gujarat 267
 
5.0%
karnataka 261
 
4.9%
madhya 259
 
4.9%
rajasthan 256
 
4.8%
maharashtra 255
 
4.8%
haryana 253
 
4.8%
Other values (8) 1918
36.0%

Most occurring characters

ValueCountFrequency (%)
a 9162
24.0%
h 3864
10.1%
r 3363
 
8.8%
n 2274
 
6.0%
e 2039
 
5.3%
t 1832
 
4.8%
d 1784
 
4.7%
i 1512
 
4.0%
l 1486
 
3.9%
s 1287
 
3.4%
Other values (21) 9588
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31868
83.4%
Uppercase Letter 5324
 
13.9%
Space Separator 999
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9162
28.7%
h 3864
12.1%
r 3363
 
10.6%
n 2274
 
7.1%
e 2039
 
6.4%
t 1832
 
5.7%
d 1784
 
5.6%
i 1512
 
4.7%
l 1486
 
4.7%
s 1287
 
4.0%
Other values (6) 3265
 
10.2%
Uppercase Letter
ValueCountFrequency (%)
P 535
10.0%
D 525
9.9%
C 519
9.7%
M 514
9.7%
K 510
9.6%
B 486
9.1%
T 471
8.8%
U 276
 
5.2%
G 267
 
5.0%
R 256
 
4.8%
Other values (4) 965
18.1%
Space Separator
ValueCountFrequency (%)
999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37192
97.4%
Common 999
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9162
24.6%
h 3864
10.4%
r 3363
 
9.0%
n 2274
 
6.1%
e 2039
 
5.5%
t 1832
 
4.9%
d 1784
 
4.8%
i 1512
 
4.1%
l 1486
 
4.0%
s 1287
 
3.5%
Other values (20) 8589
23.1%
Common
ValueCountFrequency (%)
999
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9162
24.0%
h 3864
10.1%
r 3363
 
8.8%
n 2274
 
6.0%
e 2039
 
5.3%
t 1832
 
4.8%
d 1784
 
4.7%
i 1512
 
4.0%
l 1486
 
3.9%
s 1287
 
3.4%
Other values (21) 9588
25.1%

Total Land Area (acres)
Real number (ℝ)

Distinct4235
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503.48307
Minimum10.17
Maximum999.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:43.385039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10.17
5-th percentile56.362
Q1252.95
median509.17
Q3751.25
95-th percentile946.298
Maximum999.53
Range989.36
Interquartile range (IQR)498.3

Descriptive statistics

Standard deviation285.93506
Coefficient of variation (CV)0.56791395
Kurtosis-1.2038243
Mean503.48307
Median Absolute Deviation (MAD)249.55
Skewness-0.020572007
Sum2177564.3
Variance81758.859
MonotonicityNot monotonic
2023-09-05T18:23:43.999198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
609.62 3
 
0.1%
637.96 2
 
< 0.1%
50.61 2
 
< 0.1%
207.06 2
 
< 0.1%
783.52 2
 
< 0.1%
131.86 2
 
< 0.1%
378.37 2
 
< 0.1%
837.07 2
 
< 0.1%
50.03 2
 
< 0.1%
680.4 2
 
< 0.1%
Other values (4225) 4304
99.5%
ValueCountFrequency (%)
10.17 1
< 0.1%
10.25 1
< 0.1%
10.32 1
< 0.1%
10.91 1
< 0.1%
11.32 1
< 0.1%
11.48 2
< 0.1%
11.73 1
< 0.1%
11.86 1
< 0.1%
12 1
< 0.1%
12.64 1
< 0.1%
ValueCountFrequency (%)
999.53 1
< 0.1%
999.39 1
< 0.1%
999.18 1
< 0.1%
998.99 1
< 0.1%
998.55 1
< 0.1%
998.53 1
< 0.1%
998.5 1
< 0.1%
998.12 1
< 0.1%
997.31 1
< 0.1%
997.26 1
< 0.1%

Number of Cows
Real number (ℝ)

Distinct91
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.963699
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:44.289658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q132
median55
Q377
95-th percentile96
Maximum100
Range90
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.111487
Coefficient of variation (CV)0.47506785
Kurtosis-1.1917445
Mean54.963699
Median Absolute Deviation (MAD)23
Skewness0.019617027
Sum237718
Variance681.80974
MonotonicityNot monotonic
2023-09-05T18:23:44.589952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 63
 
1.5%
25 63
 
1.5%
34 62
 
1.4%
55 61
 
1.4%
94 60
 
1.4%
29 60
 
1.4%
58 60
 
1.4%
42 59
 
1.4%
77 59
 
1.4%
28 58
 
1.3%
Other values (81) 3720
86.0%
ValueCountFrequency (%)
10 45
1.0%
11 47
1.1%
12 47
1.1%
13 50
1.2%
14 41
0.9%
15 34
0.8%
16 45
1.0%
17 44
1.0%
18 45
1.0%
19 50
1.2%
ValueCountFrequency (%)
100 44
1.0%
99 40
0.9%
98 54
1.2%
97 42
1.0%
96 51
1.2%
95 48
1.1%
94 60
1.4%
93 46
1.1%
92 50
1.2%
91 46
1.1%

Farm Size
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size263.4 KiB
Large
1462 
Medium
1439 
Small
1424 

Length

Max length6
Median length5
Mean length5.3327168
Min length5

Characters and Unicode

Total characters23064
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowLarge
3rd rowMedium
4th rowSmall
5th rowMedium

Common Values

ValueCountFrequency (%)
Large 1462
33.8%
Medium 1439
33.3%
Small 1424
32.9%

Length

2023-09-05T18:23:44.862069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T18:23:45.124417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
large 1462
33.8%
medium 1439
33.3%
small 1424
32.9%

Most occurring characters

ValueCountFrequency (%)
e 2901
12.6%
a 2886
12.5%
m 2863
12.4%
l 2848
12.3%
L 1462
6.3%
r 1462
6.3%
g 1462
6.3%
M 1439
6.2%
d 1439
6.2%
i 1439
6.2%
Other values (2) 2863
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18739
81.2%
Uppercase Letter 4325
 
18.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2901
15.5%
a 2886
15.4%
m 2863
15.3%
l 2848
15.2%
r 1462
7.8%
g 1462
7.8%
d 1439
7.7%
i 1439
7.7%
u 1439
7.7%
Uppercase Letter
ValueCountFrequency (%)
L 1462
33.8%
M 1439
33.3%
S 1424
32.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 23064
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2901
12.6%
a 2886
12.5%
m 2863
12.4%
l 2848
12.3%
L 1462
6.3%
r 1462
6.3%
g 1462
6.3%
M 1439
6.2%
d 1439
6.2%
i 1439
6.2%
Other values (2) 2863
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2901
12.6%
a 2886
12.5%
m 2863
12.4%
l 2848
12.3%
L 1462
6.3%
r 1462
6.3%
g 1462
6.3%
M 1439
6.2%
d 1439
6.2%
i 1439
6.2%
Other values (2) 2863
12.4%

Date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1278
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size283.1 KiB
28/01/2021
 
11
22/09/2022
 
10
20/04/2020
 
9
23/06/2019
 
9
21/09/2021
 
9
Other values (1273)
4277 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters43250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)3.7%

Sample

1st row17/02/2022
2nd row01/12/2021
3rd row28/02/2022
4th row09/06/2019
5th row14/12/2020

Common Values

ValueCountFrequency (%)
28/01/2021 11
 
0.3%
22/09/2022 10
 
0.2%
20/04/2020 9
 
0.2%
23/06/2019 9
 
0.2%
21/09/2021 9
 
0.2%
23/11/2022 9
 
0.2%
03/10/2021 8
 
0.2%
19/10/2020 8
 
0.2%
15/07/2021 8
 
0.2%
01/04/2020 8
 
0.2%
Other values (1268) 4236
97.9%

Length

2023-09-05T18:23:45.335667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28/01/2021 11
 
0.3%
22/09/2022 10
 
0.2%
20/04/2020 9
 
0.2%
23/06/2019 9
 
0.2%
21/09/2021 9
 
0.2%
23/11/2022 9
 
0.2%
20/01/2020 8
 
0.2%
05/06/2021 8
 
0.2%
10/12/2021 8
 
0.2%
20/09/2019 8
 
0.2%
Other values (1268) 4236
97.9%

Most occurring characters

ValueCountFrequency (%)
2 11145
25.8%
0 10725
24.8%
/ 8650
20.0%
1 6025
13.9%
9 1786
 
4.1%
6 877
 
2.0%
7 842
 
1.9%
5 837
 
1.9%
3 806
 
1.9%
4 792
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34600
80.0%
Other Punctuation 8650
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11145
32.2%
0 10725
31.0%
1 6025
17.4%
9 1786
 
5.2%
6 877
 
2.5%
7 842
 
2.4%
5 837
 
2.4%
3 806
 
2.3%
4 792
 
2.3%
8 765
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 8650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11145
25.8%
0 10725
24.8%
/ 8650
20.0%
1 6025
13.9%
9 1786
 
4.1%
6 877
 
2.0%
7 842
 
1.9%
5 837
 
1.9%
3 806
 
1.9%
4 792
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11145
25.8%
0 10725
24.8%
/ 8650
20.0%
1 6025
13.9%
9 1786
 
4.1%
6 877
 
2.0%
7 842
 
1.9%
5 837
 
1.9%
3 806
 
1.9%
4 792
 
1.8%

Product ID
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5095954
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:45.543147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8429787
Coefficient of variation (CV)0.516005
Kurtosis-1.1980782
Mean5.5095954
Median Absolute Deviation (MAD)2
Skewness-0.024827298
Sum23829
Variance8.0825282
MonotonicityNot monotonic
2023-09-05T18:23:45.738826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 479
11.1%
7 447
10.3%
9 441
10.2%
4 437
10.1%
8 435
10.1%
2 431
10.0%
1 429
9.9%
5 423
9.8%
10 402
9.3%
3 401
9.3%
ValueCountFrequency (%)
1 429
9.9%
2 431
10.0%
3 401
9.3%
4 437
10.1%
5 423
9.8%
6 479
11.1%
7 447
10.3%
8 435
10.1%
9 441
10.2%
10 402
9.3%
ValueCountFrequency (%)
10 402
9.3%
9 441
10.2%
8 435
10.1%
7 447
10.3%
6 479
11.1%
5 423
9.8%
4 437
10.1%
3 401
9.3%
2 431
10.0%
1 429
9.9%

Product Name
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size266.2 KiB
Curd
479 
Lassi
447 
Paneer
441 
Yogurt
437 
Buttermilk
435 
Other values (5)
2086 

Length

Max length10
Median length9
Mean length5.9865896
Min length4

Characters and Unicode

Total characters25892
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIce Cream
2nd rowMilk
3rd rowYogurt
4th rowCheese
5th rowButtermilk

Common Values

ValueCountFrequency (%)
Curd 479
11.1%
Lassi 447
10.3%
Paneer 441
10.2%
Yogurt 437
10.1%
Buttermilk 435
10.1%
Butter 431
10.0%
Milk 429
9.9%
Ice Cream 423
9.8%
Ghee 402
9.3%
Cheese 401
9.3%

Length

2023-09-05T18:23:45.976796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T18:23:46.286716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
curd 479
10.1%
lassi 447
9.4%
paneer 441
9.3%
yogurt 437
9.2%
buttermilk 435
9.2%
butter 431
9.1%
milk 429
9.0%
ice 423
8.9%
cream 423
8.9%
ghee 402
8.5%

Most occurring characters

ValueCountFrequency (%)
e 4601
17.8%
r 2646
 
10.2%
t 2169
 
8.4%
u 1782
 
6.9%
a 1311
 
5.1%
i 1311
 
5.1%
C 1303
 
5.0%
s 1295
 
5.0%
B 866
 
3.3%
k 864
 
3.3%
Other values (15) 7744
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20721
80.0%
Uppercase Letter 4748
 
18.3%
Space Separator 423
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4601
22.2%
r 2646
12.8%
t 2169
10.5%
u 1782
 
8.6%
a 1311
 
6.3%
i 1311
 
6.3%
s 1295
 
6.2%
k 864
 
4.2%
l 864
 
4.2%
m 858
 
4.1%
Other values (6) 3020
14.6%
Uppercase Letter
ValueCountFrequency (%)
C 1303
27.4%
B 866
18.2%
L 447
 
9.4%
P 441
 
9.3%
Y 437
 
9.2%
M 429
 
9.0%
I 423
 
8.9%
G 402
 
8.5%
Space Separator
ValueCountFrequency (%)
423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25469
98.4%
Common 423
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4601
18.1%
r 2646
 
10.4%
t 2169
 
8.5%
u 1782
 
7.0%
a 1311
 
5.1%
i 1311
 
5.1%
C 1303
 
5.1%
s 1295
 
5.1%
B 866
 
3.4%
k 864
 
3.4%
Other values (14) 7321
28.7%
Common
ValueCountFrequency (%)
423
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4601
17.8%
r 2646
 
10.2%
t 2169
 
8.4%
u 1782
 
6.9%
a 1311
 
5.1%
i 1311
 
5.1%
C 1303
 
5.0%
s 1295
 
5.0%
B 866
 
3.3%
k 864
 
3.3%
Other values (15) 7744
29.9%

Brand
Categorical

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size273.6 KiB
Amul
1053 
Mother Dairy
1010 
Raj
685 
Sudha
648 
Dodla Dairy
222 
Other values (6)
707 

Length

Max length20
Median length16
Mean length7.7574566
Min length3

Characters and Unicode

Total characters33551
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDodla Dairy
2nd rowAmul
3rd rowDodla Dairy
4th rowBritannia Industries
5th rowMother Dairy

Common Values

ValueCountFrequency (%)
Amul 1053
24.3%
Mother Dairy 1010
23.4%
Raj 685
15.8%
Sudha 648
15.0%
Dodla Dairy 222
 
5.1%
Palle2patnam 211
 
4.9%
Dynamix Dairies 106
 
2.5%
Warana 104
 
2.4%
Parag Milk Foods 102
 
2.4%
Passion Cheese 96
 
2.2%

Length

2023-09-05T18:23:46.671755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dairy 1232
20.4%
amul 1053
17.4%
mother 1010
16.7%
raj 685
11.3%
sudha 648
10.7%
dodla 222
 
3.7%
palle2patnam 211
 
3.5%
dairies 106
 
1.8%
dynamix 106
 
1.8%
warana 104
 
1.7%
Other values (7) 674
11.1%

Most occurring characters

ValueCountFrequency (%)
a 4420
 
13.2%
r 2730
 
8.1%
i 2012
 
6.0%
l 1799
 
5.4%
u 1789
 
5.3%
h 1754
 
5.2%
1726
 
5.1%
e 1703
 
5.1%
D 1666
 
5.0%
o 1532
 
4.6%
Other values (22) 12420
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25563
76.2%
Uppercase Letter 6051
 
18.0%
Space Separator 1726
 
5.1%
Decimal Number 211
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4420
17.3%
r 2730
10.7%
i 2012
 
7.9%
l 1799
 
7.0%
u 1789
 
7.0%
h 1754
 
6.9%
e 1703
 
6.7%
o 1532
 
6.0%
t 1397
 
5.5%
m 1370
 
5.4%
Other values (9) 5057
19.8%
Uppercase Letter
ValueCountFrequency (%)
D 1666
27.5%
M 1112
18.4%
A 1053
17.4%
R 685
11.3%
S 648
 
10.7%
P 409
 
6.8%
W 104
 
1.7%
F 102
 
1.7%
C 96
 
1.6%
B 88
 
1.5%
Space Separator
ValueCountFrequency (%)
1726
100.0%
Decimal Number
ValueCountFrequency (%)
2 211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31614
94.2%
Common 1937
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4420
14.0%
r 2730
 
8.6%
i 2012
 
6.4%
l 1799
 
5.7%
u 1789
 
5.7%
h 1754
 
5.5%
e 1703
 
5.4%
D 1666
 
5.3%
o 1532
 
4.8%
t 1397
 
4.4%
Other values (20) 10812
34.2%
Common
ValueCountFrequency (%)
1726
89.1%
2 211
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4420
 
13.2%
r 2730
 
8.1%
i 2012
 
6.0%
l 1799
 
5.4%
u 1789
 
5.3%
h 1754
 
5.2%
1726
 
5.1%
e 1703
 
5.1%
D 1666
 
5.0%
o 1532
 
4.6%
Other values (22) 12420
37.0%

Quantity (liters/kg)
Real number (ℝ)

Distinct4224
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.65266
Minimum1.17
Maximum999.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:46.954202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.17
5-th percentile45.516
Q1254.17
median497.55
Q3749.78
95-th percentile946.194
Maximum999.93
Range998.76
Interquartile range (IQR)495.61

Descriptive statistics

Standard deviation288.97592
Coefficient of variation (CV)0.57719841
Kurtosis-1.1912202
Mean500.65266
Median Absolute Deviation (MAD)247.63
Skewness-0.014905626
Sum2165322.7
Variance83507.08
MonotonicityNot monotonic
2023-09-05T18:23:47.245071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
318.52 3
 
0.1%
34.5 3
 
0.1%
385.32 2
 
< 0.1%
423.1 2
 
< 0.1%
68.79 2
 
< 0.1%
339.45 2
 
< 0.1%
965.9 2
 
< 0.1%
485.65 2
 
< 0.1%
345.37 2
 
< 0.1%
432.96 2
 
< 0.1%
Other values (4214) 4303
99.5%
ValueCountFrequency (%)
1.17 1
< 0.1%
1.22 1
< 0.1%
1.24 1
< 0.1%
1.58 1
< 0.1%
2.11 1
< 0.1%
2.29 1
< 0.1%
2.72 1
< 0.1%
3.1 1
< 0.1%
3.23 1
< 0.1%
3.46 1
< 0.1%
ValueCountFrequency (%)
999.93 1
< 0.1%
999.87 1
< 0.1%
999.82 1
< 0.1%
999.8 1
< 0.1%
999.78 1
< 0.1%
999.16 1
< 0.1%
998.55 1
< 0.1%
998.52 1
< 0.1%
997.98 1
< 0.1%
997.91 1
< 0.1%

Price per Unit
Real number (ℝ)

Distinct3409
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.785938
Minimum10.03
Maximum99.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:47.592859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10.03
5-th percentile14.056
Q132.46
median54.4
Q377.46
95-th percentile95.198
Maximum99.99
Range89.96
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.002815
Coefficient of variation (CV)0.47462571
Kurtosis-1.197723
Mean54.785938
Median Absolute Deviation (MAD)22.39
Skewness0.0093223587
Sum236949.18
Variance676.14638
MonotonicityNot monotonic
2023-09-05T18:23:48.278781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.72 5
 
0.1%
94.88 4
 
0.1%
46.01 4
 
0.1%
29.74 4
 
0.1%
31.05 4
 
0.1%
58.57 4
 
0.1%
24.45 4
 
0.1%
41.05 4
 
0.1%
22.47 4
 
0.1%
66.12 4
 
0.1%
Other values (3399) 4284
99.1%
ValueCountFrequency (%)
10.03 1
< 0.1%
10.05 1
< 0.1%
10.09 1
< 0.1%
10.11 2
< 0.1%
10.15 1
< 0.1%
10.16 2
< 0.1%
10.17 1
< 0.1%
10.19 1
< 0.1%
10.28 1
< 0.1%
10.32 1
< 0.1%
ValueCountFrequency (%)
99.99 1
< 0.1%
99.96 2
< 0.1%
99.94 1
< 0.1%
99.84 1
< 0.1%
99.78 2
< 0.1%
99.73 1
< 0.1%
99.71 1
< 0.1%
99.7 1
< 0.1%
99.68 1
< 0.1%
99.65 1
< 0.1%

Total Value
Real number (ℝ)

Distinct4324
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27357.845
Minimum42.5165
Maximum99036.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:48.725018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum42.5165
5-th percentile1843.367
Q19946.8145
median21869.653
Q340954.441
95-th percentile70700.452
Maximum99036.37
Range98993.853
Interquartile range (IQR)31007.626

Descriptive statistics

Standard deviation21621.052
Coefficient of variation (CV)0.79030535
Kurtosis0.014246727
Mean27357.845
Median Absolute Deviation (MAD)14194.94
Skewness0.87398539
Sum1.1832268 × 108
Variance4.6746987 × 108
MonotonicityNot monotonic
2023-09-05T18:23:49.075112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15929.8755 2
 
< 0.1%
19064.128 1
 
< 0.1%
14688.26 1
 
< 0.1%
17256.0762 1
 
< 0.1%
10514.3223 1
 
< 0.1%
16689.9376 1
 
< 0.1%
34924.3664 1
 
< 0.1%
8040.2208 1
 
< 0.1%
60949.6079 1
 
< 0.1%
1064.272 1
 
< 0.1%
Other values (4314) 4314
99.7%
ValueCountFrequency (%)
42.5165 1
< 0.1%
46.0428 1
< 0.1%
47.43 1
< 0.1%
59.2722 1
< 0.1%
70.6056 1
< 0.1%
73.2962 1
< 0.1%
93.67 1
< 0.1%
97.5096 1
< 0.1%
106.32 1
< 0.1%
114.4176 1
< 0.1%
ValueCountFrequency (%)
99036.3696 1
< 0.1%
97631.1921 1
< 0.1%
96528.5579 1
< 0.1%
96137.34 1
< 0.1%
94594.4951 1
< 0.1%
94246.0402 1
< 0.1%
94083.759 1
< 0.1%
93714.673 1
< 0.1%
93567.2472 1
< 0.1%
93254.265 1
< 0.1%

Shelf Life (days)
Real number (ℝ)

Distinct146
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.12763
Minimum1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:49.620873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median22
Q330
95-th percentile101
Maximum150
Range149
Interquartile range (IQR)20

Descriptive statistics

Standard deviation30.272114
Coefficient of variation (CV)1.0392921
Kurtosis4.1148805
Mean29.12763
Median Absolute Deviation (MAD)11
Skewness2.0824962
Sum125977
Variance916.40091
MonotonicityNot monotonic
2023-09-05T18:23:50.211792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 264
 
6.1%
13 196
 
4.5%
12 177
 
4.1%
14 174
 
4.0%
6 159
 
3.7%
26 158
 
3.7%
5 154
 
3.6%
28 150
 
3.5%
29 137
 
3.2%
30 131
 
3.0%
Other values (136) 2625
60.7%
ValueCountFrequency (%)
1 119
2.8%
2 106
2.5%
5 154
3.6%
6 159
3.7%
7 264
6.1%
8 105
 
2.4%
9 101
 
2.3%
10 111
2.6%
11 117
2.7%
12 177
4.1%
ValueCountFrequency (%)
150 5
0.1%
149 3
 
0.1%
148 8
0.2%
147 7
0.2%
146 6
0.1%
145 5
0.1%
144 5
0.1%
143 6
0.1%
142 2
 
< 0.1%
141 3
 
0.1%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size284.0 KiB
Refrigerated
2459 
Frozen
1035 
Ambient
402 
Polythene Packet
 
225
Tetra Pack
 
204

Length

Max length16
Median length12
Mean length10.213179
Min length6

Characters and Unicode

Total characters44172
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrozen
2nd rowTetra Pack
3rd rowRefrigerated
4th rowFrozen
5th rowRefrigerated

Common Values

ValueCountFrequency (%)
Refrigerated 2459
56.9%
Frozen 1035
23.9%
Ambient 402
 
9.3%
Polythene Packet 225
 
5.2%
Tetra Pack 204
 
4.7%

Length

2023-09-05T18:23:50.571684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T18:23:50.897095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
refrigerated 2459
51.7%
frozen 1035
21.8%
ambient 402
 
8.5%
polythene 225
 
4.7%
packet 225
 
4.7%
tetra 204
 
4.3%
pack 204
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 9693
21.9%
r 6157
13.9%
t 3515
 
8.0%
a 3092
 
7.0%
i 2861
 
6.5%
R 2459
 
5.6%
f 2459
 
5.6%
g 2459
 
5.6%
d 2459
 
5.6%
n 1662
 
3.8%
Other values (14) 7356
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38989
88.3%
Uppercase Letter 4754
 
10.8%
Space Separator 429
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9693
24.9%
r 6157
15.8%
t 3515
 
9.0%
a 3092
 
7.9%
i 2861
 
7.3%
f 2459
 
6.3%
g 2459
 
6.3%
d 2459
 
6.3%
n 1662
 
4.3%
o 1260
 
3.2%
Other values (8) 3372
 
8.6%
Uppercase Letter
ValueCountFrequency (%)
R 2459
51.7%
F 1035
21.8%
P 654
 
13.8%
A 402
 
8.5%
T 204
 
4.3%
Space Separator
ValueCountFrequency (%)
429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43743
99.0%
Common 429
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9693
22.2%
r 6157
14.1%
t 3515
 
8.0%
a 3092
 
7.1%
i 2861
 
6.5%
R 2459
 
5.6%
f 2459
 
5.6%
g 2459
 
5.6%
d 2459
 
5.6%
n 1662
 
3.8%
Other values (13) 6927
15.8%
Common
ValueCountFrequency (%)
429
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9693
21.9%
r 6157
13.9%
t 3515
 
8.0%
a 3092
 
7.0%
i 2861
 
6.5%
R 2459
 
5.6%
f 2459
 
5.6%
g 2459
 
5.6%
d 2459
 
5.6%
n 1662
 
3.8%
Other values (14) 7356
16.7%

Production Date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1405
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Memory size283.1 KiB
13/01/2022
 
9
03/09/2019
 
9
06/09/2022
 
9
09/11/2021
 
9
11/07/2019
 
8
Other values (1400)
4281 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters43250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique240 ?
Unique (%)5.5%

Sample

1st row27/12/2021
2nd row03/10/2021
3rd row14/01/2022
4th row15/05/2019
5th row17/10/2020

Common Values

ValueCountFrequency (%)
13/01/2022 9
 
0.2%
03/09/2019 9
 
0.2%
06/09/2022 9
 
0.2%
09/11/2021 9
 
0.2%
11/07/2019 8
 
0.2%
18/01/2021 8
 
0.2%
22/05/2019 8
 
0.2%
30/12/2021 8
 
0.2%
12/07/2020 8
 
0.2%
06/11/2019 8
 
0.2%
Other values (1395) 4241
98.1%

Length

2023-09-05T18:23:51.198530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13/01/2022 9
 
0.2%
06/09/2022 9
 
0.2%
09/11/2021 9
 
0.2%
03/09/2019 9
 
0.2%
12/07/2020 8
 
0.2%
06/09/2021 8
 
0.2%
10/11/2022 8
 
0.2%
06/11/2019 8
 
0.2%
03/09/2020 8
 
0.2%
30/12/2021 8
 
0.2%
Other values (1395) 4241
98.1%

Most occurring characters

ValueCountFrequency (%)
2 10986
25.4%
0 10759
24.9%
/ 8650
20.0%
1 5999
13.9%
9 1879
 
4.3%
3 992
 
2.3%
8 889
 
2.1%
7 795
 
1.8%
6 773
 
1.8%
5 772
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34600
80.0%
Other Punctuation 8650
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10986
31.8%
0 10759
31.1%
1 5999
17.3%
9 1879
 
5.4%
3 992
 
2.9%
8 889
 
2.6%
7 795
 
2.3%
6 773
 
2.2%
5 772
 
2.2%
4 756
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 8650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10986
25.4%
0 10759
24.9%
/ 8650
20.0%
1 5999
13.9%
9 1879
 
4.3%
3 992
 
2.3%
8 889
 
2.1%
7 795
 
1.8%
6 773
 
1.8%
5 772
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10986
25.4%
0 10759
24.9%
/ 8650
20.0%
1 5999
13.9%
9 1879
 
4.3%
3 992
 
2.3%
8 889
 
2.1%
7 795
 
1.8%
6 773
 
1.8%
5 772
 
1.8%

Expiration Date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1441
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size283.1 KiB
27/07/2019
 
9
15/01/2021
 
9
01/02/2022
 
9
04/03/2020
 
9
10/03/2021
 
8
Other values (1436)
4281 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters43250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique273 ?
Unique (%)6.3%

Sample

1st row21/01/2022
2nd row25/10/2021
3rd row13/02/2022
4th row26/07/2019
5th row28/10/2020

Common Values

ValueCountFrequency (%)
27/07/2019 9
 
0.2%
15/01/2021 9
 
0.2%
01/02/2022 9
 
0.2%
04/03/2020 9
 
0.2%
10/03/2021 8
 
0.2%
09/01/2019 8
 
0.2%
19/04/2020 8
 
0.2%
18/01/2021 8
 
0.2%
03/08/2019 8
 
0.2%
26/08/2019 8
 
0.2%
Other values (1431) 4241
98.1%

Length

2023-09-05T18:23:51.419613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27/07/2019 9
 
0.2%
01/02/2022 9
 
0.2%
04/03/2020 9
 
0.2%
15/01/2021 9
 
0.2%
06/02/2022 8
 
0.2%
04/02/2019 8
 
0.2%
01/03/2020 8
 
0.2%
19/05/2021 8
 
0.2%
10/04/2019 8
 
0.2%
15/08/2019 8
 
0.2%
Other values (1431) 4241
98.1%

Most occurring characters

ValueCountFrequency (%)
2 11034
25.5%
0 10781
24.9%
/ 8650
20.0%
1 5964
13.8%
9 1819
 
4.2%
3 1043
 
2.4%
8 820
 
1.9%
5 819
 
1.9%
6 796
 
1.8%
4 767
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34600
80.0%
Other Punctuation 8650
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11034
31.9%
0 10781
31.2%
1 5964
17.2%
9 1819
 
5.3%
3 1043
 
3.0%
8 820
 
2.4%
5 819
 
2.4%
6 796
 
2.3%
4 767
 
2.2%
7 757
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 8650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11034
25.5%
0 10781
24.9%
/ 8650
20.0%
1 5964
13.8%
9 1819
 
4.2%
3 1043
 
2.4%
8 820
 
1.9%
5 819
 
1.9%
6 796
 
1.8%
4 767
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11034
25.5%
0 10781
24.9%
/ 8650
20.0%
1 5964
13.8%
9 1819
 
4.2%
3 1043
 
2.4%
8 820
 
1.9%
5 819
 
1.9%
6 796
 
1.8%
4 767
 
1.8%

Quantity Sold (liters/kg)
Real number (ℝ)

Distinct806
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.09503
Minimum1
Maximum960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:51.832286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.2
Q169
median189
Q3374
95-th percentile678
Maximum960
Range959
Interquartile range (IQR)305

Descriptive statistics

Standard deviation217.02418
Coefficient of variation (CV)0.87476231
Kurtosis0.25000228
Mean248.09503
Median Absolute Deviation (MAD)138
Skewness0.98198255
Sum1073011
Variance47099.495
MonotonicityNot monotonic
2023-09-05T18:23:52.118834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 41
 
0.9%
2 37
 
0.9%
8 27
 
0.6%
9 26
 
0.6%
4 25
 
0.6%
5 25
 
0.6%
13 24
 
0.6%
6 22
 
0.5%
48 22
 
0.5%
7 22
 
0.5%
Other values (796) 4054
93.7%
ValueCountFrequency (%)
1 41
0.9%
2 37
0.9%
3 18
0.4%
4 25
0.6%
5 25
0.6%
6 22
0.5%
7 22
0.5%
8 27
0.6%
9 26
0.6%
10 20
0.5%
ValueCountFrequency (%)
960 1
 
< 0.1%
956 1
 
< 0.1%
949 3
0.1%
945 1
 
< 0.1%
940 1
 
< 0.1%
934 1
 
< 0.1%
931 1
 
< 0.1%
930 2
< 0.1%
928 1
 
< 0.1%
926 1
 
< 0.1%

Price per Unit (sold)
Real number (ℝ)

Distinct3446
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.77914
Minimum5.21
Maximum104.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:52.404845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.21
5-th percentile14.216
Q132.64
median54.14
Q377.46
95-th percentile95.336
Maximum104.51
Range99.3
Interquartile range (IQR)44.82

Descriptive statistics

Standard deviation26.19279
Coefficient of variation (CV)0.47815264
Kurtosis-1.1578943
Mean54.77914
Median Absolute Deviation (MAD)22.26
Skewness0.014238974
Sum236919.78
Variance686.06227
MonotonicityNot monotonic
2023-09-05T18:23:52.728994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.3 6
 
0.1%
73.22 5
 
0.1%
40.1 5
 
0.1%
27.5 4
 
0.1%
25.47 4
 
0.1%
15.52 4
 
0.1%
32.97 4
 
0.1%
45.86 4
 
0.1%
17.74 4
 
0.1%
49.08 4
 
0.1%
Other values (3436) 4281
99.0%
ValueCountFrequency (%)
5.21 1
< 0.1%
5.61 2
< 0.1%
5.94 2
< 0.1%
6.12 1
< 0.1%
6.58 1
< 0.1%
6.77 1
< 0.1%
6.78 1
< 0.1%
6.87 1
< 0.1%
6.95 1
< 0.1%
7.11 1
< 0.1%
ValueCountFrequency (%)
104.51 1
< 0.1%
104.15 1
< 0.1%
104.06 1
< 0.1%
103.89 1
< 0.1%
103.61 1
< 0.1%
103.57 1
< 0.1%
103.49 1
< 0.1%
103.2 1
< 0.1%
103.19 1
< 0.1%
103.11 1
< 0.1%
Distinct4304
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13580.265
Minimum12.54
Maximum89108.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:53.009273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.54
5-th percentile342.796
Q12916.65
median8394.54
Q319504.55
95-th percentile44801.764
Maximum89108.9
Range89096.36
Interquartile range (IQR)16587.9

Descriptive statistics

Standard deviation14617.009
Coefficient of variation (CV)1.0763419
Kurtosis3.0207264
Mean13580.265
Median Absolute Deviation (MAD)6621.9
Skewness1.6916456
Sum58734648
Variance2.1365696 × 108
MonotonicityNot monotonic
2023-09-05T18:23:53.342839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2646.6 2
 
< 0.1%
6096 2
 
< 0.1%
18895.8 2
 
< 0.1%
4010.4 2
 
< 0.1%
3906 2
 
< 0.1%
8053.5 2
 
< 0.1%
7081.02 2
 
< 0.1%
5004 2
 
< 0.1%
129.78 2
 
< 0.1%
4875.12 2
 
< 0.1%
Other values (4294) 4305
99.5%
ValueCountFrequency (%)
12.54 1
< 0.1%
14.99 1
< 0.1%
16.3 1
< 0.1%
17.46 1
< 0.1%
19.66 1
< 0.1%
20.45 1
< 0.1%
21.12 1
< 0.1%
22.29 1
< 0.1%
23.89 1
< 0.1%
24.02 1
< 0.1%
ValueCountFrequency (%)
89108.9 1
< 0.1%
85913.52 1
< 0.1%
85352.94 1
< 0.1%
85036.41 1
< 0.1%
84838.46 1
< 0.1%
83934.9 1
< 0.1%
81350.52 1
< 0.1%
80237 1
< 0.1%
78360.4 1
< 0.1%
78214.5 1
< 0.1%
Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size278.1 KiB
Delhi
499 
Chandigarh
489 
Bihar
284 
Maharashtra
271 
Kerala
267 
Other values (10)
2515 

Length

Max length14
Median length11
Mean length8.825896
Min length5

Characters and Unicode

Total characters38172
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMadhya Pradesh
2nd rowKerala
3rd rowMadhya Pradesh
4th rowRajasthan
5th rowJharkhand

Common Values

ValueCountFrequency (%)
Delhi 499
11.5%
Chandigarh 489
11.3%
Bihar 284
 
6.6%
Maharashtra 271
 
6.3%
Kerala 267
 
6.2%
Tamil Nadu 267
 
6.2%
Uttar Pradesh 267
 
6.2%
Karnataka 264
 
6.1%
West Bengal 264
 
6.1%
Telangana 251
 
5.8%
Other values (5) 1202
27.8%

Length

2023-09-05T18:23:53.656570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 515
 
9.6%
delhi 499
 
9.3%
chandigarh 489
 
9.1%
bihar 284
 
5.3%
maharashtra 271
 
5.0%
kerala 267
 
5.0%
tamil 267
 
5.0%
nadu 267
 
5.0%
uttar 267
 
5.0%
bengal 264
 
4.9%
Other values (8) 1981
36.9%

Most occurring characters

ValueCountFrequency (%)
a 9136
23.9%
h 3786
 
9.9%
r 3348
 
8.8%
n 2225
 
5.8%
e 2060
 
5.4%
t 1815
 
4.8%
d 1762
 
4.6%
l 1548
 
4.1%
i 1539
 
4.0%
s 1284
 
3.4%
Other values (21) 9669
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31755
83.2%
Uppercase Letter 5371
 
14.1%
Space Separator 1046
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9136
28.8%
h 3786
11.9%
r 3348
 
10.5%
n 2225
 
7.0%
e 2060
 
6.5%
t 1815
 
5.7%
d 1762
 
5.5%
l 1548
 
4.9%
i 1539
 
4.8%
s 1284
 
4.0%
Other values (6) 3252
 
10.2%
Uppercase Letter
ValueCountFrequency (%)
B 548
10.2%
K 531
9.9%
M 519
9.7%
T 518
9.6%
P 515
9.6%
D 499
9.3%
C 489
9.1%
N 267
 
5.0%
U 267
 
5.0%
W 264
 
4.9%
Other values (4) 954
17.8%
Space Separator
ValueCountFrequency (%)
1046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37126
97.3%
Common 1046
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9136
24.6%
h 3786
10.2%
r 3348
 
9.0%
n 2225
 
6.0%
e 2060
 
5.5%
t 1815
 
4.9%
d 1762
 
4.7%
l 1548
 
4.2%
i 1539
 
4.1%
s 1284
 
3.5%
Other values (20) 8623
23.2%
Common
ValueCountFrequency (%)
1046
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9136
23.9%
h 3786
 
9.9%
r 3348
 
8.8%
n 2225
 
5.8%
e 2060
 
5.4%
t 1815
 
4.8%
d 1762
 
4.6%
l 1548
 
4.1%
i 1539
 
4.0%
s 1284
 
3.4%
Other values (21) 9669
25.3%

Sales Channel
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
Retail
1478 
Wholesale
1476 
Online
1371 

Length

Max length9
Median length6
Mean length7.023815
Min length6

Characters and Unicode

Total characters30378
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWholesale
2nd rowWholesale
3rd rowOnline
4th rowOnline
5th rowRetail

Common Values

ValueCountFrequency (%)
Retail 1478
34.2%
Wholesale 1476
34.1%
Online 1371
31.7%

Length

2023-09-05T18:23:53.860621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T18:23:54.179996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
retail 1478
34.2%
wholesale 1476
34.1%
online 1371
31.7%

Most occurring characters

ValueCountFrequency (%)
e 5801
19.1%
l 5801
19.1%
a 2954
9.7%
i 2849
9.4%
n 2742
9.0%
R 1478
 
4.9%
t 1478
 
4.9%
W 1476
 
4.9%
h 1476
 
4.9%
o 1476
 
4.9%
Other values (2) 2847
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26053
85.8%
Uppercase Letter 4325
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5801
22.3%
l 5801
22.3%
a 2954
11.3%
i 2849
10.9%
n 2742
10.5%
t 1478
 
5.7%
h 1476
 
5.7%
o 1476
 
5.7%
s 1476
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
R 1478
34.2%
W 1476
34.1%
O 1371
31.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 30378
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5801
19.1%
l 5801
19.1%
a 2954
9.7%
i 2849
9.4%
n 2742
9.0%
R 1478
 
4.9%
t 1478
 
4.9%
W 1476
 
4.9%
h 1476
 
4.9%
o 1476
 
4.9%
Other values (2) 2847
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5801
19.1%
l 5801
19.1%
a 2954
9.7%
i 2849
9.4%
n 2742
9.0%
R 1478
 
4.9%
t 1478
 
4.9%
W 1476
 
4.9%
h 1476
 
4.9%
o 1476
 
4.9%
Other values (2) 2847
9.4%
Distinct808
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.06867
Minimum0
Maximum976
Zeros18
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:54.448823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q166
median191
Q3387
95-th percentile714
Maximum976
Range976
Interquartile range (IQR)321

Descriptive statistics

Standard deviation223.62087
Coefficient of variation (CV)0.88714266
Kurtosis0.11768276
Mean252.06867
Median Absolute Deviation (MAD)145
Skewness0.96230075
Sum1090197
Variance50006.293
MonotonicityNot monotonic
2023-09-05T18:23:54.826753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 33
 
0.8%
8 33
 
0.8%
1 32
 
0.7%
7 31
 
0.7%
10 30
 
0.7%
21 26
 
0.6%
4 24
 
0.6%
5 23
 
0.5%
9 22
 
0.5%
14 22
 
0.5%
Other values (798) 4049
93.6%
ValueCountFrequency (%)
0 18
0.4%
1 32
0.7%
2 33
0.8%
3 14
0.3%
4 24
0.6%
5 23
0.5%
6 16
0.4%
7 31
0.7%
8 33
0.8%
9 22
0.5%
ValueCountFrequency (%)
976 1
 
< 0.1%
968 1
 
< 0.1%
962 1
 
< 0.1%
955 1
 
< 0.1%
948 3
0.1%
945 1
 
< 0.1%
944 1
 
< 0.1%
941 1
 
< 0.1%
932 1
 
< 0.1%
930 1
 
< 0.1%
Distinct3432
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.826143
Minimum10.02
Maximum99.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:55.112245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10.02
5-th percentile14.804
Q132.91
median56.46
Q379.01
95-th percentile95.734
Maximum99.99
Range89.97
Interquartile range (IQR)46.1

Descriptive statistics

Standard deviation26.30145
Coefficient of variation (CV)0.47113141
Kurtosis-1.2333583
Mean55.826143
Median Absolute Deviation (MAD)23.11
Skewness-0.041864116
Sum241448.07
Variance691.76626
MonotonicityNot monotonic
2023-09-05T18:23:55.481228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.28 4
 
0.1%
96.27 4
 
0.1%
45.63 4
 
0.1%
57.39 4
 
0.1%
11.63 4
 
0.1%
29.73 4
 
0.1%
40.89 4
 
0.1%
53.4 4
 
0.1%
83.01 4
 
0.1%
94.08 4
 
0.1%
Other values (3422) 4285
99.1%
ValueCountFrequency (%)
10.02 1
< 0.1%
10.03 2
< 0.1%
10.07 1
< 0.1%
10.09 1
< 0.1%
10.1 1
< 0.1%
10.11 1
< 0.1%
10.17 1
< 0.1%
10.19 1
< 0.1%
10.21 2
< 0.1%
10.25 1
< 0.1%
ValueCountFrequency (%)
99.99 1
< 0.1%
99.97 1
< 0.1%
99.96 1
< 0.1%
99.93 1
< 0.1%
99.92 2
< 0.1%
99.88 2
< 0.1%
99.87 1
< 0.1%
99.86 1
< 0.1%
99.85 2
< 0.1%
99.83 2
< 0.1%
Distinct3833
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.10782
Minimum20.02
Maximum199.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-09-05T18:23:55.809174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.02
5-th percentile28.99
Q164.28
median108.34
Q3153.39
95-th percentile189.812
Maximum199.95
Range179.93
Interquartile range (IQR)89.11

Descriptive statistics

Standard deviation51.501035
Coefficient of variation (CV)0.47201965
Kurtosis-1.1766584
Mean109.10782
Median Absolute Deviation (MAD)44.44
Skewness0.013757116
Sum471891.32
Variance2652.3566
MonotonicityNot monotonic
2023-09-05T18:23:56.175897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.41 4
 
0.1%
157.81 4
 
0.1%
126.54 3
 
0.1%
173.77 3
 
0.1%
119.65 3
 
0.1%
191.7 3
 
0.1%
66.62 3
 
0.1%
26.59 3
 
0.1%
36.73 3
 
0.1%
167.14 3
 
0.1%
Other values (3823) 4293
99.3%
ValueCountFrequency (%)
20.02 1
< 0.1%
20.04 2
< 0.1%
20.09 1
< 0.1%
20.12 2
< 0.1%
20.14 1
< 0.1%
20.15 1
< 0.1%
20.18 1
< 0.1%
20.19 1
< 0.1%
20.21 2
< 0.1%
20.23 1
< 0.1%
ValueCountFrequency (%)
199.95 1
< 0.1%
199.92 1
< 0.1%
199.81 1
< 0.1%
199.77 1
< 0.1%
199.71 2
< 0.1%
199.68 1
< 0.1%
199.59 1
< 0.1%
199.47 1
< 0.1%
199.45 1
< 0.1%
199.43 1
< 0.1%

Interactions

2023-09-05T18:23:37.860190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:56.855359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:00.305576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:03.748692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:07.001430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:10.448090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:13.679615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:17.233072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:20.680816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:24.147733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:27.729255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:31.064958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:34.457880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:38.127063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:57.135619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:00.565210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:04.004479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:07.301420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:10.700256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:14.162333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:17.495486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:20.948072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:24.429108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:27.984131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:31.326349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:34.732819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:38.389147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:57.388594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:00.804647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:04.255207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:07.552285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:10.935897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:14.399980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:17.745294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:21.214517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:24.684209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:28.217953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:31.574180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:34.993780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:38.643067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:57.634567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:01.037107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:04.503433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:07.818682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:11.165632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:14.646076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:18.002933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:21.464178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:24.927851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:28.476692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:31.816545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:35.256597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:38.910447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:57.902067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:01.286640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:04.752859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:08.064333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:11.417440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:14.899411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:18.266634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:21.727545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:25.181550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:28.759348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:32.077311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:35.511626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:39.173394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:58.150155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:01.535681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:04.987602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:08.319092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:11.649285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:15.163607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:18.515580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:22.012170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:25.410125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:29.001834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:32.332623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:35.777906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:39.461924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:58.417186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:01.802234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:05.242549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:08.581874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:11.916639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:15.416276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:18.780792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:22.261789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:25.669304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:29.248275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:32.608592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:36.046490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:39.781402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:58.690844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:02.066128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:05.516482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:08.868814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:12.166589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:15.683808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:19.065411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:22.569364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:25.949540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:29.514219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:32.881545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:36.311716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:40.046207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:58.951939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:02.307178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:05.755745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:09.118056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:12.416775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:15.946743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:19.353783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:22.829343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:26.203171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:29.779121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:33.133242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:36.562262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:40.378531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:59.200843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:02.553120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:06.000122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:09.386036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:12.663261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:16.194638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:19.611592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:23.080850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:26.444136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:30.032886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:33.393928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:36.819067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:40.675021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:59.459397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:02.798992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:06.252591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:09.632278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:12.900103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:16.446227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:19.867691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:23.329259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:26.685322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:30.282557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:33.651105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:37.062690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:40.948332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:22:59.736914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:03.051585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:06.500241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:09.897144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:13.166016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:16.701180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:20.127941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:23.627120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:27.208450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:30.529581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:33.910947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:37.328390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:41.222903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:00.008211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:03.472429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:06.743447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:10.164761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:13.412108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:16.962223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:20.394620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:23.879634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:27.453634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:30.791123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:34.162504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-05T18:23:37.575088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-05T18:23:56.562342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Total Land Area (acres)Number of CowsProduct IDQuantity (liters/kg)Price per UnitTotal ValueShelf Life (days)Quantity Sold (liters/kg)Price per Unit (sold)Approx. Total Revenue(INR)Quantity in Stock (liters/kg)Minimum Stock Threshold (liters/kg)Reorder Quantity (liters/kg)LocationFarm SizeProduct NameBrandStorage ConditionCustomer LocationSales Channel
Total Land Area (acres)1.000-0.010-0.008-0.0010.0170.0070.006-0.0380.014-0.0240.0240.0110.0140.0000.0260.0000.0000.0000.0000.016
Number of Cows-0.0101.0000.010-0.004-0.006-0.0140.0010.009-0.0070.004-0.0210.0030.0290.0000.0000.0180.0190.0000.0130.041
Product ID-0.0080.0101.000-0.0280.003-0.013-0.112-0.0230.001-0.020-0.015-0.005-0.0030.0000.0121.0000.4350.8000.0180.029
Quantity (liters/kg)-0.001-0.004-0.0281.000-0.0100.762-0.0040.654-0.0080.5680.684-0.0100.0030.0180.0000.0170.0260.0000.0000.015
Price per Unit0.017-0.0060.003-0.0101.0000.569-0.0060.0010.9940.427-0.0110.0120.0080.0170.0000.0000.0000.0100.0220.022
Total Value0.007-0.014-0.0130.7620.5691.000-0.0050.5330.5670.7490.5470.010-0.0000.0060.0190.0090.0090.0000.0000.031
Shelf Life (days)0.0060.001-0.112-0.004-0.006-0.0051.0000.011-0.0060.004-0.004-0.012-0.0270.0110.0240.5240.3130.5620.0170.000
Quantity Sold (liters/kg)-0.0380.009-0.0230.6540.0010.5330.0111.0000.0010.8760.024-0.0030.0080.0000.0000.0090.0000.0190.0160.000
Price per Unit (sold)0.014-0.0070.001-0.0080.9940.567-0.0060.0011.0000.430-0.0110.0140.0050.0000.0110.0080.0000.0000.0200.000
Approx. Total Revenue(INR)-0.0240.004-0.0200.5680.4270.7490.0040.8760.4301.0000.0380.0070.0030.0250.0000.0000.0050.0000.0120.030
Quantity in Stock (liters/kg)0.024-0.021-0.0150.684-0.0110.547-0.0040.024-0.0110.0381.000-0.0020.0130.0000.0320.0220.0000.0000.0000.000
Minimum Stock Threshold (liters/kg)0.0110.003-0.005-0.0100.0120.010-0.012-0.0030.0140.007-0.0021.0000.0170.0000.0000.0000.0000.0000.0100.000
Reorder Quantity (liters/kg)0.0140.029-0.0030.0030.008-0.000-0.0270.0080.0050.0030.0130.0171.0000.0190.0240.0130.0000.0280.0360.007
Location0.0000.0000.0000.0180.0170.0060.0110.0000.0000.0250.0000.0000.0191.0000.0000.0000.0160.0000.0000.010
Farm Size0.0260.0000.0120.0000.0000.0190.0240.0000.0110.0000.0320.0000.0240.0001.0000.0120.0260.0090.0150.017
Product Name0.0000.0181.0000.0170.0000.0090.5240.0090.0080.0000.0220.0000.0130.0000.0121.0000.4350.8000.0180.029
Brand0.0000.0190.4350.0260.0000.0090.3130.0000.0000.0050.0000.0000.0000.0160.0260.4351.0000.2690.0200.007
Storage Condition0.0000.0000.8000.0000.0100.0000.5620.0190.0000.0000.0000.0000.0280.0000.0090.8000.2691.0000.0000.000
Customer Location0.0000.0130.0180.0000.0220.0000.0170.0160.0200.0120.0000.0100.0360.0000.0150.0180.0200.0001.0000.051
Sales Channel0.0160.0410.0290.0150.0220.0310.0000.0000.0000.0300.0000.0000.0070.0100.0170.0290.0070.0000.0511.000

Missing values

2023-09-05T18:23:41.694575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-05T18:23:42.529126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LocationTotal Land Area (acres)Number of CowsFarm SizeDateProduct IDProduct NameBrandQuantity (liters/kg)Price per UnitTotal ValueShelf Life (days)Storage ConditionProduction DateExpiration DateQuantity Sold (liters/kg)Price per Unit (sold)Approx. Total Revenue(INR)Customer LocationSales ChannelQuantity in Stock (liters/kg)Minimum Stock Threshold (liters/kg)Reorder Quantity (liters/kg)
0Telangana310.8496Medium17/02/20225Ice CreamDodla Dairy222.4085.7219064.128025Frozen27/12/202121/01/2022782.24575.68Madhya PradeshWholesale21519.5564.03
1Uttar Pradesh19.1944Large01/12/20211MilkAmul687.4842.6129293.522822Tetra Pack03/10/202125/10/202155839.2421895.92KeralaWholesale12943.17181.10
2Tamil Nadu581.6924Medium28/02/20224YogurtDodla Dairy503.4836.5018377.020030Refrigerated14/01/202213/02/202225633.818655.36Madhya PradeshOnline24715.10140.83
3Telangana908.0089Small09/06/20193CheeseBritannia Industries823.3626.5221835.507272Frozen15/05/201926/07/201960128.9217380.92RajasthanOnline22274.5057.68
4Maharashtra861.9521Medium14/12/20208ButtermilkMother Dairy147.7783.8512390.514511Refrigerated17/10/202028/10/202014583.0712045.15JharkhandRetail276.0233.40
5Telangana87.6451Medium07/01/20196CurdRaj593.9285.5450803.91685Refrigerated03/01/201908/01/20197484.756271.50GujaratRetail51955.60139.59
6Karnataka73.2874Small05/08/20223CheeseDynamix Dairies757.1436.8327885.466283Frozen14/06/202205/09/202241032.6613390.60KarnatakaOnline34749.82105.86
7Bihar67.6177Medium14/02/20199PaneerMother Dairy203.3629.085913.708814Refrigerated06/01/201920/01/20191529.09436.35HaryanaOnline18810.9025.14
8West Bengal72.3176Medium02/08/20206CurdRaj949.1222.7921630.44486Refrigerated06/06/202012/06/202086024.7721302.20JharkhandWholesale8985.7132.03
9Telangana413.2436Large04/03/20228ButtermilkMother Dairy385.6442.0816227.73129Refrigerated03/02/202212/02/202210846.154984.20Tamil NaduRetail27793.6166.26
LocationTotal Land Area (acres)Number of CowsFarm SizeDateProduct IDProduct NameBrandQuantity (liters/kg)Price per UnitTotal ValueShelf Life (days)Storage ConditionProduction DateExpiration DateQuantity Sold (liters/kg)Price per Unit (sold)Approx. Total Revenue(INR)Customer LocationSales ChannelQuantity in Stock (liters/kg)Minimum Stock Threshold (liters/kg)Reorder Quantity (liters/kg)
4315Chandigarh147.6910Large07/02/20211MilkSudha513.9734.5617762.80322Polythene Packet17/01/202119/01/202132332.6610549.18MaharashtraRetail19033.84173.39
4316Telangana449.8828Small05/01/20218ButtermilkAmul877.5783.5673329.74928Refrigerated10/11/202018/11/202085584.4872230.40GujaratRetail2234.4354.45
4317Kerala662.4080Medium06/11/20198ButtermilkSudha250.9626.816728.237614Refrigerated07/10/201921/10/201922929.506755.50Uttar PradeshWholesale2130.8439.60
4318Madhya Pradesh641.6674Small20/01/20202ButterWarana970.9237.8636759.031226Frozen13/12/201908/01/20202642.541106.04ChandigarhWholesale94436.2332.27
4319Uttar Pradesh269.6391Small05/03/20203CheeseDynamix Dairies517.6990.2846737.053285Frozen07/02/202002/05/202023986.9120771.49KarnatakaWholesale27851.96106.10
4320Delhi748.7189Medium24/02/20226CurdMother Dairy554.9088.4549080.90505Refrigerated16/02/202221/02/202235287.2030694.40Uttar PradeshOnline20298.0733.53
4321Jharkhand385.9129Large14/05/20224YogurtPalle2patnam818.3355.3545294.565523Refrigerated22/03/202214/04/20226858.393970.52KeralaRetail75087.41114.37
4322Chandigarh311.5465Small05/01/20206CurdMother Dairy583.5692.6154043.49167Refrigerated04/01/202011/01/202014189.4612613.86HaryanaRetail44233.47153.66
4323Maharashtra890.5590Small25/10/20226CurdRaj3.1015.3047.43007Refrigerated02/10/202209/10/2022210.5621.12JharkhandWholesale158.25160.84
4324Rajasthan492.8658Large20/01/20191MilkMother Dairy820.5049.3140458.85501Polythene Packet06/01/201907/01/201941744.5518577.35Madhya PradeshOnline40322.34189.63